




"""simple docstring"""






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 	a__      (							unittest.TestCase	):





						@property
						def    __UpperCamelCase  (				self   :						str)    ->							List[Any]:




											"""simple docstring"""







											torch.manual_seed(0)
											_lowerCAmelCase:Dict						=	UNetaDModel(
											    block_out_channels=(32, 64)  ,layers_per_block=2  ,sample_size=32  ,in_channels=3  ,out_channels=3  ,down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')  ,up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')  ,)
											return model





						@property
						def    __UpperCamelCase  (				self   :						List[Any])    ->							List[str]:




											"""simple docstring"""







											torch.manual_seed(0)
											_lowerCAmelCase:Dict						=	VQModel(
											    block_out_channels=[32, 64]  ,in_channels=3  ,out_channels=3  ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,latent_channels=3  ,)
											return model





						@property
						def    __UpperCamelCase  (				self   :						Optional[int])    ->							Any:




											"""simple docstring"""







											torch.manual_seed(0)
											_lowerCAmelCase:List[Any]						=	CLIPTextConfig(
											    bos_token_id=0  ,eos_token_id=2  ,hidden_size=32  ,intermediate_size=37  ,layer_norm_eps=1E-05  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1000  ,)
											return CLIPTextModel(a_)







						def    __UpperCamelCase  (				self   :						Dict)    ->							List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]						=	self.dummy_uncond_unet
											_lowerCAmelCase:List[Any]						=	DDIMScheduler()
											_lowerCAmelCase:Tuple						=	self.dummy_vq_model

											_lowerCAmelCase:Optional[int]						=	LDMPipeline(unet=a_  ,vqvae=a_  ,scheduler=a_)
											ldm.to(a_)
											ldm.set_progress_bar_config(disable=a_)

											_lowerCAmelCase:Optional[Any]						=	torch.manual_seed(0)
											_lowerCAmelCase:Tuple						=	ldm(generator=a_  ,num_inference_steps=2  ,output_type='''numpy''').images

											_lowerCAmelCase:Dict						=	torch.manual_seed(0)
											_lowerCAmelCase:Any						=	ldm(generator=a_  ,num_inference_steps=2  ,output_type='''numpy'''  ,return_dict=a_)[0]

											_lowerCAmelCase:Any						=	image[0, -3:, -3:, -1]
											_lowerCAmelCase:str						=	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											_lowerCAmelCase:Union[str, Any]						=	np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
											_lowerCAmelCase:str						=	1E-2 if torch_device != "mps" else 3E-2

											assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance




@slow
@require_torch
class 	a__      (							unittest.TestCase	):





						def    __UpperCamelCase  (				self   :						int)    ->							List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]						=	LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256''')
											ldm.to(a_)
											ldm.set_progress_bar_config(disable=a_)

											_lowerCAmelCase:List[Any]						=	torch.manual_seed(0)
											_lowerCAmelCase:Any						=	ldm(generator=a_  ,num_inference_steps=5  ,output_type='''numpy''').images

											_lowerCAmelCase:str						=	image[0, -3:, -3:, -1]

											assert image.shape == (1, 256, 256, 3)
											_lowerCAmelCase:Tuple						=	np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
											_lowerCAmelCase:int						=	1E-2 if torch_device != "mps" else 3E-2

											assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance













'''simple docstring'''






import random
from typing import Any






def   __A    (				a_    :	list						):
		for _ in range(len(a_						)						):
				lowerCAmelCase  :     List[Any]       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase  :     Tuple       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= data[b], data[a]
		return data


if __name__ == "__main__":
					lowerCAmelCase									=   [0, 1, 2, 3, 4, 5, 6, 7]
					lowerCAmelCase									=   ["""python""", """says""", """hello""", """!"""]
					print("""Fisher-Yates Shuffle:""")
					print("""List""", integers, strings)
					print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



